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Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python Yousri El Fattah, Reza Bagheri

(ebook) (audiobook) (audiobook) Język publikacji: angielski
Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python Yousri El Fattah, Reza Bagheri - okladka książki

Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python Yousri El Fattah, Reza Bagheri - okladka książki

Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python Yousri El Fattah, Reza Bagheri - audiobook MP3

Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python Yousri El Fattah, Reza Bagheri - audiobook CD

Autorzy:
Yousri El Fattah, Reza Bagheri
Serie wydawnicze:
Hands-on
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
686
Dostępne formaty:
     PDF
     ePub
Ebook
139,00 zł

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Do przechowalni

This practical guide explores Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation using real-world data. You’ll learn Bayesian networks, conditional independence, structural causal models (SCM), and intervention-based reasoning for causal analysis. The book explains how graphical models support probabilistic inference, decision-making, and knowledge representation across healthcare, economics, epidemiology, finance, and social sciences.

You’ll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearl’s do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. You’ll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.

Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, you’ll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.

O autorze książki

Yousri El Fattah is the CEO of Causal Computing and has taught courses on artificial intelligence and on control systems at multiple universities, contributed many research and development projects on causal modeling for companies in aerospace and industrial automation, and was a senior scientist in information technology at Rockwell and at Teledyne Technologies. El Fattah is a published author of a book on Learning Systems as well as numerous technical articles in encyclopedia, conference proceedings, and journals including Machine Learning, Artificial Intelligence, IEEE and ASME Transactions. He has a Ph.D.in information and computer sciences as well as a Ph.D. in aeronautical engineering.

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